Despite the importance and versatility of linear mixed effects models (LME), they have seldom been used in whole brain imaging analyses due to the computational requirement. Here, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole brain voxelwise imaging LME analyses possible. We demonstrate the equivalency of statistical power and control of type I errors between FEMA and classical LME, whilst showing an order of magnitude improvement in the speed of FEMA compared to classical LME. By applying FEMA on diffusion images and resting state functional connectivity matrices from the ABCD StudySM release 4.0 data, we show voxelwise annualized changes in fractional anisotropy (FA) and functional connectomes in early adolescence, highlighting a critical time of establishing associations among cortical and subcortical regions.

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Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study® sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study® sample.

Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive DevelopmentSM; Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924, 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability from 0.00 to 0.61), respectively. Incorporating measured genetic relatedness and using the full ABCD Study®sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study®sample.

Objective: The Accelerator program for Discovery in Brain disorders using Stem cells (ADBS) is a longitudinal study on five cohorts of patients with major psychiatric disorders from genetically high-risk families, their unaffected first-degree relatives, and healthy subjects. We describe the ADBS protocols for acquisition, quality assurance (QA), and quality check (QC) for multimodal magnetic resonance brain imaging studies. Methods:We describe the acquisition and QC protocols for structural, functional, and diffusion images. For QA, we acquire proton density and functional images on phantoms, along with repeated scans on human volunteer. We describe the analysis of phantom data and test-retest reliability of volumetric and diffusion measures.Results: Analysis of acquired phantom data shows linearity of proton density signal with increasing proton fraction, and an overall stability of various spatial and temporal QA measures. Examination of dice coefficient and statistical analyses of coefficient of variation in test-retest data on the human volunteer showed consistency of volumetric and diffusivity measures at whole-brain, regional, and voxel-level. Conclusion:The described acquisition and QA-QC procedures can yield consistent and reliable quantitative measures. It is expected that this longitudinal Pravesh Parekh and Gaurav V. Bhalerao should be considered joint first author.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

ImportancePremenstrual disorders are heritable, clinically heterogenous, with a range of affective spectrum comorbidities. It is unclear whether genetic predispositions to affective spectrum disorders or other major psychiatric disorders are associated with symptoms of premenstrual disorders.ObjectiveTo assesss whether symptoms of premenstrual disorders are associated with the genetic liability for major psychiatric disorders, as indexed by polygenic risk scores (PRSs).Design, Setting, and ParticipantsWomen from the Norwegian Mother, Father and Child Cohort Study were included in this genetic association study. PRSs were used to determine whether genetic liability for major depression, bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder, and autism spectrum disorder were associated with the symptoms of premenstrual disorders, using the PRS for height as a somatic comparator. The sample was recruited across Norway between June 1999 and December 2008, and analyses were performed from July 1 to October 14, 2022.Main Outcomes and MeasuresThe symptoms of premenstrual disorders were assessed at recruitment at week 15 of pregnancy with self-reported severity of depression and irritability before menstruation. Logistic regression was applied to test for the association between the presence of premenstrual disorder symptoms and the PRSs for major psychiatric disorders.ResultsThe mean (SD) age of 56 725 women included in the study was 29.0 (4.6) years. Premenstrual disorder symptoms were present in 12 316 of 56 725 participants (21.7%). The symptoms of premenstrual disorders were associated with the PRSs for major depression (β = 0.13; 95% CI, 0.11-0.15; P = 1.21 × 10−36), bipolar disorder (β = 0.07; 95% CI, 0.05-0.09; P = 1.74 × 10−11), attention deficit/hyperactivity disorder (β = 0.07; 95% CI, 0.04-0.09; P = 1.58 × 10−9), schizophrenia (β = 0.11; 95% CI, 0.09-0.13; P = 7.61 × 10−25), and autism spectrum disorder (β = 0.03; 95% CI, 0.01-0.05; P = .02) but not with the PRS for height. The findings were confirmed in a subsample of women without a history of psychiatric diagnosis.ConclusionsThe results of this genetic association study show that genetic liability for both affective spectrum disorder and major psychiatric disorders was associated with symptoms of premenstrual disorders, indicating that premenstrual disorders have overlapping genetic foundations with major psychiatric disorders.

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Magnetic resonance imaging (MRI) has been a popular and useful non-invasive method to map patterns of brain structure and function to complex human traits. Recently published observations in multiple large-scale studies cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional MRI, which seems to account for little behavioral variability. We leverage baseline data from thousands of children in the Adolescent Brain Cognitive Development (ABCD) Study to inform the replication sample size required with both univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 42 individuals in the replication sample for working memory-related functional MRI, and ~100 subjects for structural MRI. Even with 50 subjects in the discovery sample, prediction can be adequately powered with 105 subjects in the replication sample for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many investigators' research programs and grants.